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Abstract:

Methods and apparatuses for detecting faults and optimizing phase
currents in an electromechanical energy converter are disclosed. An
example method comprises: measuring a current of a phase of the
electromechanical energy converter, modeling the electromechanical energy
converter with the current measurement input into a field reconstruction
module, calculating a flux linkage of the electromechanical energy
converter, comparing the flux linkage with a flux linkage from a no fault
electromechanical energy converter, and optimizing the current of the
phase of the electromechanical energy converter in response to the
comparison. Other embodiments are described and claimed.

Claims:

1. A method for detecting faults and optimizing phase currents in an
electromechanical energy converter, the method comprising: measuring a
current of a phase of the electromechanical energy converter; modeling
the electromechanical energy converter with the current measurement input
into a field reconstruction module; calculating a flux linkage of the
electromechanical energy converter; comparing the flux linkage with a
flux linkage from a no fault electromechanical energy converter; and
optimizing the current of the phase of the electromechanical energy
converter in response to the comparison.

2. The method of claim 1, further comprising simultaneously detecting
faults and optimizing phase currents in all the phases of the
electromechanical energy converter.

4. The method of claim 3, wherein calculating the flux linkage of the
electromechanical energy converter comprises: finding magnetic field
components due to the permanent magnets and the stator excitation in the
middle of the airgap of the permanent magnet synchronous machine; and
finding the contribution of the magnetic field components passing through
the stator tooth of the permanent magnet synchronous machine.

6. The method of claim 3, wherein modeling the electromechanical energy
converter with the current measurement input into a field reconstruction
module comprises: obtaining a normal flux density distribution at a rotor
position due to the permanent magnet of the permanent magnet synchronous
machine; obtaining a tangential flux density distribution at the rotor
position due to the permanent magnet of the permanent magnet synchronous
machine; reconstructing a flux density due to all the phases of the
permanent magnet synchronous machine; reconstructing the flux density in
a layer of interest; and repeating the modeling for all positions of the
rotor of the permanent magnet synchronous machine.

7. The method of claim 1, wherein optimizing the current of the phase of
the electromechanical energy converter in response to the comparison
comprises maximizing average torque, maximizing average torque with
minimum torque ripple, or minimizing torque ripple.

8. An apparatus for detecting faults and optimizing phase currents in an
electromechanical energy converter, the apparatus comprising: one or more
processors; and one or more memory units coupled to the processors, the
apparatus being configured to: measure a current of a phase of the
electromechanical energy converter; model the electromechanical energy
converter with the current measurement input into a field reconstruction
module; calculate a flux linkage of the electromechanical energy
converter; compare the flux linkage with a flux linkage from a no fault
electromechanical energy converter; and optimize the current of the phase
of the electromechanical energy converter in response to the comparison.

9. The apparatus of claim 8, wherein the apparatus is further configured
to simultaneously detect faults and optimize phase currents in all the
phases of the electromechanical energy converter.

11. The apparatus of claim 10, wherein the apparatus being configured to
calculate the flux linkage of the electromechanical energy converter
comprises: finding magnetic field components due to the permanent magnets
and the stator excitation in the middle of the airgap of the permanent
magnet synchronous machine; and finding the contribution of the magnetic
field components passing through the stator tooth of the permanent magnet
synchronous machine.

13. The apparatus of claim 10, wherein the apparatus being configured to
model the electromechanical energy converter with the current measurement
input into a field reconstruction module comprises: obtaining a normal
flux density distribution at a rotor position due to the permanent magnet
of the permanent magnet synchronous machine; obtaining a tangential flux
density distribution at the rotor position due to the permanent magnet of
the permanent magnet synchronous machine; reconstructing a flux density
due to all the phases of the permanent magnet synchronous machine;
reconstructing the flux density in a layer of interest; and repeating the
modeling for all positions of the rotor of the permanent magnet
synchronous machine.

14. The apparatus of claim 8, wherein the apparatus being configured to
optimize the current of the phase of the electromechanical energy
converter in response to the comparison comprises maximizing average
torque, maximizing average torque with minimum torque ripple, or
minimizing torque ripple.

Description:

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of the filing date of U.S.
provisional patent application No. 61/226,667, incorporated herein by
reference, which was filed on Jul. 17, 2009, by the same inventors of
this application.

FIELD OF THE INVENTION

[0003] The present invention generally relates to fault management in
multi-phase permanent magnet synchronous machines. More particularly, the
invention relates to fault detection and post fault treatment using the
field reconstruction method.

BACKGROUND OF THE INVENTION

[0004] The embodiment described herein relates generally to the field of
fault detection in permanent magnet synchronous machines (PMSM) and the
subsequent management of the faults after detection. The field
reconstruction method (FRM) is utilized in a magnetic flux observer
system to (a) detect faults and (b) optimize the torque in a PMSM in real
time.

[0005] In order to detect faults in an electromechanical energy converter,
knowledge of the distribution and behavior of the magnetic field is
necessary. Present tools for magnetic field analysis mainly use the
finite element analysis (FEA) to find the distribution of the magnetic
field in the electromechanical energy conversion unit. Although accurate,
the main problem with FEA based methods is the amount of time which is
necessary to carry out the task. Therefore FEA methods are inappropriate
for real time control schemes. Also, FEA methods increase the
computational expense of the whole system. In addition, there are issues
in flux estimation using conventional methods as they use voltage
integration which leads in significant numerical errors in low speed
applications. One of the main areas in which the magnetic field analysis
is applicable is the observation of the magnetic flux passing through the
stator teeth in an electrical machine. Normally, a set of search coils
are mounted on the machine to sense induced voltages. Then the data from
the sensors would be fed into the DSP to be converted into corresponding
flux linkage values. The magnetic flux observer method here gives the
same results with acceptable accuracy while using only the phase
currents. For the purpose of calibration, the estimated flux values may
be compared to actual values measured using the search coils. Once the
magnetic flux observer is calibrated it may be used to detect faults such
as inter-turn short-circuits, rotor eccentricity, and PM demagnetization.

SUMMARY

[0006] In one respect, disclosed is an apparatus for detecting faults and
optimizing phase currents in an electromechanical energy converter, the
apparatus comprising: one or more processors and one or more memory units
coupled to the processors. The apparatus is configured to: measure a
current of a phase of the electromechanical energy converter, model the
electromechanical energy converter with the current measurement input
into a field reconstruction module, calculate a flux linkage of the
electromechanical energy converter, compare the flux linkage with a flux
linkage from a no fault electromechanical energy converter, and optimize
the current of the phase of the electromechanical energy converter in
response to the comparison.

[0007] In another respect, disclosed is a method for detecting faults and
optimizing phase currents in an electromechanical energy converter, the
method comprising: measuring a current of a phase of the
electromechanical energy converter, modeling the electromechanical energy
converter with the current measurement input into a field reconstruction
module, calculating a flux linkage of the electromechanical energy
converter, comparing the flux linkage with a flux linkage from a no fault
electromechanical energy converter, and optimizing the current of the
phase of the electromechanical energy converter in response to the
comparison.

[0008] Numerous additional embodiments are also possible.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Features and advantages of the present invention will become
apparent from the appended claims, the following detailed description of
one or more example embodiments, and the corresponding figures.

[0010] FIG. 1 is a schematic illustration of a 5-phase, 6-pole, 30 slot
surface mounted permanent magnet machine, in accordance with some
embodiments.

[0011] FIG. 2 is a flow diagram illustrating the field reconstruction
method, in accordance with some embodiments.

[0012] FIG. 3 is a graph depicting the tangential field components from
FEA and FRM, in accordance with some embodiments.

[0013] FIG. 4 is a graph depicting the normal field components from FEA
and FRM, in accordance with some embodiments.

[0014] FIG. 5 is a graph comparing the torque obtained from FEA and FRM,
in accordance with some embodiments.

[0015] FIG. 6(a) is a schematic illustration showing the magnetic field
distribution in the first quadrant of the PMSM, in accordance with some
embodiments.

[0016] FIG. 6(b) is a schematic illustration showing the magnetic flux
component projection into the middle of a stator tooth, in accordance
with some embodiments.

[0017] FIG. 7 is a schematic illustration showing flux assignment to
stator teeth, in accordance with some embodiments.

[0018]FIG. 8 is a graph of the phase "A" flux linkage calculated using
the flux observer based on the FRM compared to that of the FEA, in
accordance with some embodiments.

[0019]FIG. 9 is a schematic illustration showing a full bridge converter
for one phase of the PMSM, in accordance with some embodiments.

[0020] FIG. 10 is a flow diagram illustrating the fault detection scheme,
in accordance with some embodiments.

[0021] FIG. 11 is a schematic illustrating the arrangement of the phase
windings in the machine for a single phase open-circuit fault which takes
place in phase "A", in accordance with some embodiments.

[0022] FIG. 12 is a graph illustrating the fault signature for the case of
the healthy operation and phase "A" open-circuit fault, in accordance
with some embodiments.

[0023] FIG. 13 depicts the winding arrangement and the fault signature for
the case of an adjacent open-circuit fault on phases "A" and "C", in
accordance with some embodiments.

[0024] FIG. 14 depicts the winding arrangement and fault signature for the
case of a non-adjacent double open-circuit, in accordance with some
embodiments.

[0025] FIG. 15 depicts the winding arrangement and the fault signature for
the case of a non-adjacent open-circuit fault on phases "A", "B", and
"C", in accordance with some embodiments.

[0026] FIG. 16 depicts the winding arrangement and the fault signature for
the case of a triple open-circuit fault on phases "A", "B", and "D", in
accordance with some embodiments.

[0027] FIG. 17 is a graph of the frequency spectrum of the fault signature
for single magnet demagnetization, in accordance with some embodiments.

[0028] FIG. 18 is a graph of the frequency spectrum of the fault signature
for double magnet demagnetization, in accordance with some embodiments.

[0029] FIG. 19 illustrates the comparison of the magnetic flux
distribution within a healthy machine and the magnetic flux distribution
within a PMSM with an eccentric rotor, in accordance with some
embodiments.

[0030]FIG. 20 depicts the flux passing the third stator tooth and the
twentieth stator tooth in the case of an eccentric rotor compared with
that of the healthy machine, in accordance with some embodiments.

[0031] FIGS. 21(a) and (b) represent the stator current and torque for the
case of sinusoidal excitation, respectively, of the 5-phase PMSM machine
from FIG. 1, in accordance with some embodiments.

[0032] FIGS. 22(a) and (b) represent the stator current and torque for the
case of optimal excitation, respectively, of the 5-phase PMSM machine
from FIG. 1, in accordance with some embodiments.

[0033] FIG. 23(a) illustrates the optimal current waveforms in the case of
an open-circuit fault in phase "D", in accordance with some embodiments.

[0034] FIG. 23(b) depicts the resulting toque in the case where the
optimized currents are applied to the remaining healthy phases, in
accordance with some embodiments.

[0035] FIGS. 24(a) and (b) show the output mechanical torque of the
machine for healthy sinusoidal excitation and partially demagnetized,
respectively, in accordance with some embodiments.

[0036] FIGS. 25(a) and (b) depict the optimal stator phase currents and
the output torque, respectively, of the machine with partially
demagnetized magnets, in accordance with some embodiments.

[0037] FIGS. 26(a) and (b) depict the sinusoidal excitation stator
currents and the corresponding output torque, respectively, for the case
with the eccentric rotor, in accordance with some embodiments.

[0038] FIGS. 27(a) and (b) depict the optimal stator phase currents and
the output torque, respectively, of the machine with an eccentric rotor,
in accordance with some embodiments.

[0039] FIG. 28 is a block diagram illustrating an apparatus for detecting
faults and optimizing phase currents in an electromechanical energy
converter, in accordance with some embodiments.

DETAILED DESCRIPTION OF ONE OR MORE EMBODIMENTS

[0040] The drawing figures are not necessarily to scale and certain
features may be shown exaggerated in scale or in somewhat generalized or
schematic form in the interest of clarity and conciseness. In the
description which follows like parts may be marked throughout the
specification and drawing with the same reference numerals. The foregoing
description of the figures is provided for a more complete understanding
of the drawings. It should be understood, however, that the embodiments
are not limited to the precise arrangements and configurations shown.
Although the design and use of various embodiments are discussed in
detail below, it should be appreciated that the present invention
provides many inventive concepts that may be embodied in a wide variety
of contexts. The specific aspects and embodiments discussed herein are
merely illustrative of ways to make and use the invention, and do not
limit the scope of the invention. It would be impossible or impractical
to include all of the possible embodiments and contexts of the invention
in this disclosure. Upon reading this disclosure, many alternative
embodiments of the present invention will be apparent to persons of
ordinary skill in the art.

[0041] Fault tolerance has become a design criterion for adjustable speed
motor drives (ASMD) which are used in high impact applications. In simple
terms, a fault tolerant ASMD is expected to continue its intended
function in the event of a failure compliment to its remaining
components. Although some ASMD such as switched reluctance motor drives
enjoy an inherent modular and hence fault resilient architecture, special
precautions have to be undertaken for multi-phase PMSM and induction
motor (IM) drives to secure continued service. In case of a PMSM motor
drive, maximum operating temperature is also limited due to the thermal
limitations of the permanent magnets and stator windings. Based on this,
the faults in a PMSM should be detected and cleared immediately to avoid
further damage to the system. On the other hand, in some applications,
the continuity of the service is of great importance so the next step
would be to calculate the best excitation possible for the remaining
active phases of the machine to harvest maximum torque. A wide variety of
research has been done on the techniques of fault detection. Most of the
research solely focuses on the fault itself rather than the aftermath.
Also, most of the methods are only applicable to the specific faults on
the stator windings and are not applicable to other types of faults.

[0042] After a fault is detected in a PMSM, the torque needs to be
optimized in order to maximize the output torque per ampere while
minimizing the torque ripple. Torque optimization of a PMSM can be
achieved using conventional techniques. These conventional methods use
different optimization algorithms based on magnetic field analysis
resulting from finite element analysis. As the finite element procedures
are time consuming, these methods are not adequate for real time control.

[0043] An alternative for real time control of fault detection and
optimization of a PMSM utilizes the field reconstruction method. The FRM
uses the field created by a single slot along with the field generated by
the permanent magnets on the rotor to find the field distribution and
electromagnetic force components for any arbitrary rotor position and
excitation. FRM may be used to estimate the flux linking each stator
tooth for the purpose of detecting faults. Additionally, the FRM may be
used in conjunction with optimization methods to find the optimal
excitation strategy for each fault which would be used after the fault is
cleared.

[0044] The use of the field reconstruction method is demonstrated in a 10
hp, 5-phase, 6-pole, 30 slot surface mounted permanent magnet machine.
The model of the machine is shown in FIG. 1 with one of the stator teeth
105 and one of the six poles 110 identified. The model is used to compare
the results from the FRM with those from the FEA. The model is simulated
using the commercial finite element package MAGNET from Infolytica
Corporation. In the model, it is assumed that there are no deformations
in the permanent magnets or stator teeth due to the internal forces, that
the stator windings are concentrated, and that there are no end coil
effects.

[0045] In order to calculate the torque, the magnetic field components
need to be known. For an unsaturated PMSM, the magnetization curve can be
considered to be linear and as such the superposition rule is applied to
the magnetic field components as expressed in equations (1) and (2),

Bt=Btpm+Bts (1)

Bn=Bnpm+Bns (2)

where Bnpm, Btpm, Bns, and Bts denote the normal and
tangential field components due to the permanent magnets and stator
currents respectively. The resultant magnetic field created by the stator
windings is the sum of the field created by each individual stator slot
current. The normal and tangential field components due to the stator
currents can be written as expressed in equations (3) and (4),

B n s = k = 1 L B nsk ( 3 ) B ts =
k = 1 L B tsk ( 4 ) ##EQU00001##

where L denotes the number of stator teeth. In order to evaluate
equations (3) and (4), the local flux densities created by the current in
the kth slot are expressed in equations (5) and (6),

Btsk(φs)=If1(φs) (5)

Bnsk(φs)=If2(φs) (6)

where f1 and f2 are associated with the geometry. A single
magneto-static FEA is needed to find these basis functions. Having these
basis functions for a typical slot first carrying current I0,
equations (5) and (6) can be rewritten as expressed in equations (7) and
(8).

Btsk=(I/I0)Bts0(φ-kγ) (7)

Bnsk=(I/I0)Bns0(φ-kγ) (8)

Therefore by performing a single off-line FEA for a single slot
contribution of the stator, the rotating field components can be
calculated for any normal working condition. In the second step, the
permanent magnet contribution to the magnetic field over one pole pitch
is computed using an FEA analysis for the unexcited stator condition.
Having these two components, the magnetic field components can be
obtained in the middle of the air gap. The field reconstruction flowchart
is shown in FIG. 2. Processing begins at block 205 where the normal and
tangential flux density components due to the current flowing through one
stator slot, islot, are obtained using the FEA. At block 210, the
basis functions from the field distribution profile are determined. At
block 215, the normal and tangential flux density distributions at one
rotor position due to the permanent magnet are obtained. The rotor
position is then initialized to zero at block 220. Next, at block 225,
the flux density distributions due to phase currents, iabc, are
reconstructed. Then at block 230, the flux density distribution in the
layer of interest is reconstructed. At decision block 235, it is checked
to see if the angular position, θ, is equal to 360°. If not,
then processing continues to block 240 where the angular position is
incremented by Δθ. Processing then continues by repeating the
loop of blocks 225, 230, and 235 until θ is equal to 360° at
which point processing ends at 299.

[0046] In order to verify the accuracy of this method the tangential and
normal components of the magnetic field in the middle of the airgap
obtained from FRM are compared to those from FEA. FIGS. 3 and 4 depict
the accuracy of the tangential and normal magnetic field reconstruction
method, respectively. As can be seen, the magnetic fields calculated
using the FRM are very similar to those calculated using the FEA.

[0047] There are a variety of ways to calculate the electromagnetic force
in the electrical machines. The Maxwell Stress Tensor (MST) method is one
such way. According to MST, the force component densities in the air gap
can be calculated using the formulae expressed in equations (9) and (10),

ft=BnBt/μ0 (9)

fn=(Bn2-Bt2)/2μ0 (10)

in which Bn and Bt are the normal and the tangential components
of the magnetic flux density, respectively. Therefore, the force
components would be as expressed in equations (11) and (12),

F t = Γ f → t l ( 11 )
##EQU00002##

Fn=∫02πfnrdfφ (12)

where, Γ is the integration contour. Thus, for torque calculations,
magnetic field components should be known. The MST method is quite
effective in determining the magnetic field components provided that the
FEA solutions are precise. The torque comparison for the FEA and the FRM
is shown in FIG. 5. As can be seen, the torque from the FRM matches that
from the FEA.

[0048] In order to calculate the flux linkage of each phase, the magnetic
flux flowing through each stator tooth needs to be first computed. In the
first step, using the field components in the middle of the airgap
between the rotor and stator, the flux components in the stator teeth are
calculated. The magnetic field distribution in the first quadrant of the
model from FIG. 1 is shown in FIG. 6(a). According to this figure a
dominant majority of the flux lines that exist in the airgap would enter
the stator tooth from the top surface. So, the flux in each stator tooth
can be calculated using the magnetic fields in the airgap. There would be
a slight error in this calculation because of the leakage flux (i.e. some
flux lines would enter the stator tooth from the side surfaces instead of
the top surface). These flux lines are not accounted for in the
calculation and therefore cause a slight error.

[0049] The first step in the analysis is to create the FRM model of the
machine. Using the field components in the middle of the airgap, the
magnetic flux passing through each stator tooth may be calculated. For
this purpose a portion of the contour passing through the middle of the
airgap and covering a stator tooth is used. The span of magnetic field
components corresponding to each stator tooth can be determined as
expressed in equation (13a),

S = 360 ° L ( 13 a ) ##EQU00003##

where L denotes the number of stator teeth. In the PMSM of FIG. 1, there
are 30 stator teeth so the magnetic components of each 12° span
are designated to one tooth. Having partitioned the airgap, the next step
is to project the magnetic field components to the axis passing through
the middle of each stator tooth. This process is schematically shown in
FIG. 6(b) and can be formulated using equation (13b),

where, φi and θj are the positions of the field
components in the airgap and the position of the projection axes in the
model respectively. The indices i=1 . . . K and j=1 . . . L refer to the
number of field components solutions in the airgap covering one stator
tooth and the respective stator teeth order respectively. Having the
normal field components, the flux in the airgap, which is almost equal to
the flux in the stator tooth, can be calculated as expressed in equation
(14),

Φ = ∫ ∫ S B → proj S →
( 14 ) ##EQU00005##

The integration of equation (14) is performed on the surface which is
concentric to the rotor surface and passes through the stator teeth.

[0050] In calculating the 5 phase flux linkages in the model of FIG. 1,
the flux can be calculated for one pole and then tripled to get the phase
flux linkage. FIG. 7 depicts the flux related to phase "A" in the first
quadrant which is the A1-A2 set. The flux linkage of this winding is as
expressed in equation (15).

λ.sub.Z1-A1=N(Φ2+Φ3+Φ4+Φ5+.PHI-
.6) (15)

So, the phase "A" flux linkage is as expressed in equation (16),

λA=3N(Φ2+Φ3+Φ4+Φ5+Φ.s-
ub.6) (16)

where, N represents the number of conductors in each coil. Equation (16)
can be generalized into the following form as expressed in equation (17)
for a machine with q stator tooth per pole per phase and 2P magnetic
poles (P represents the number of magnetic pole pairs).

λ A = PN * k = 1 q Φ k ( 17 )
##EQU00006##

The same analysis can be carried out for phases "B", "C", "D", and "E".
FIG. 8 depicts the phase "A" flux linkage calculated using the proposed
method compared to that of the FEA. As can be seen, the flux observer
based on the FRM is quite accurate compared to the FEA and also much
faster.

[0051] The most frequently occurring faults in permanent magnet motor
drives can be classified into faults related to electrical structure or
faults related to mechanical structure. A multi phase drive, in which
each phase is regarded as a single module is the most redundant design
for fault tolerant purposes. These modules should have minimal impact on
each other so that the failure in one does not affect the others. This
modular approach requires separate single phase bridges and thus minimal
electrical interaction. In the case of magnetic coupling between the
phases, fault current in one can induce voltages in the remaining phases,
which in turn causes problems especially in the control process and thus
minimal magnetic interaction is also required. Additionally, the stator
outer surface should be cooled down properly to minimize thermal
interaction. To achieve these goals, the PMSM should be excited using
separate full bridges per each phase. In this case if any of the stator
phases or their corresponding power electronic converter experiences a
fault, it can be disengaged from the healthy components. Based on this
arrangement, possible faults on the power electronics components and
electrical machine phases can be illustrated as shown in FIG. 9 of a full
bridge converter for one phase of the PMSM. The faults can be classified
into short and open-circuit faults in the DC link 901, short and
open-circuit faults in switches 902, 904, 908, and 910, short and
open-circuit faults in the diodes 903, 905, 909, and 911, open-circuit
fault in the machine phase winding 906, and partial and complete
short-circuit faults in the machine phase winding 907. Besides these
faults, there are a set of faults that can happen in the sensors
measuring current, voltage, and position. These types of fault will
undermine the control accuracy and functionality. The second set of
faults that can happen in an electromechanical energy converter are from
a magnetic or mechanical nature and include partial demagnetization of
the rotor magnets and static rotor shaft eccentricity.

[0052] Open-circuit faults: The open-circuit faults can either happen in
the power electronics components or in the machine stator windings.
Following an open-circuit in one of the stator phases, the current
flowing into that phase will be zero. The open-circuit faults will
deteriorate performance of the machine in terms of magnetic field
generation and distribution which results in the loss of synchronism and
a net drop in the torque. It must be noted that the open-circuit can be
the result of an inter-turn short-circuit in stator winding which is
detected and has forced the controller to disconnect the faulty stator
phase. The FRM-based modeling of the machine would not be any different
from that of the healthy machine. In case of an open-circuit, the current
corresponding to that phase will be zero in the field reconstruction
model. Accordingly, the magnetic field distribution and hence the flux
linking each of the phases can be monitored for fault detection purposes.

[0053] Rotor partial demagnetization: In case of a PMSM drive, besides the
regular monitoring of the current and voltage levels, maximum operating
temperature is also limited due to the thermal limitations of the
permanent magnets and stator windings. This thermal limit can be
potentially exceeded due to poor ventilation (excessive heat) or
excessive currents (extreme magnetic field) caused during short-circuits.
These events would change the magnetic properties of the permanent
magnets resulting in potential demagnetization. This demagnetization will
affect the performance of the machine by a great extent. The main causes
for demagnetization can be classified into thermal shock, mechanical
shock, and magnetic shock. The permanent magnet can maintain its
properties as long as its temperature is within the safe range. In the
case of a short-circuit or overheating due to poor cooling, the
temperature of the PMSM increases and can result in partial
demagnetization. In this case, a degradation of the coercive force in the
permanent magnet may occur. Variation of temperature can degrade the
performance of the permanent magnets. Also, mechanical shock can
partially or entirely damage the permanent magnets. Magnet degradation,
especially for Nd--Fe--B permanent magnets, can occur in the case of
inclined fields which normally lead to a phase displacement between the
magnetization direction of the magnet and the applied field during
machine operation. The demagnetization of the magnets has attracted
considerable attention because demagnetization of the magnets in high
power applications is one of the main issues. In most detection
techniques, the harmonic contents of the stator current are used to
detect the demagnetization. However, this method is not able to
distinguish between the harmonics due to demagnetization and those caused
by eccentricity.

[0054] Rotor eccentricity: The eccentricity of the rotor is one of the
major faults in electrical machines due to the faulty bearings,
unbalanced mass, and shaft bending. This type of eccentricity can be in
the horizontal, the vertical, or both directions. The rotor eccentricity
is equivalent to introducing unequal airgap between the stator and the
rotor, thus causing an asymmetric distribution of the magnetic field in
the airgap. The eccentricity of the rotor can be classified into either a
static eccentricity or a dynamic eccentricity. In case of a static
eccentricity, the position of minimal radial airgap length is constant
during the rotation of the rotor meaning that the rotor is shifted
towards one side but it does not move during rotation. In the case of
static eccentricity, the amplitude of the forces applied to the stator
teeth would alter and result in unbalanced radial forces. This can cause
magnetic and dynamic issues resulting in vibrations, noise, and torque
pulsations. There are various methods of eccentricity fault detection,
such as current spectrum analysis. In the case of a dynamic eccentricity,
the center of the shaft rotates and the airgap changes dynamically during
rotation.

[0055] In order to detect the faults in the stator phases, a combination
of the flux based and current based techniques are considered. The
technique includes the injection of the measured currents into the field
reconstruction module and determination of the flux linkages due to the
current and then comparison of the resulting fluxes with those of the
healthy, no-fault machine. In case of a fault, the flux linkages can be
investigated to determine the type of the fault. The fault detection
scheme is illustrated in the block diagram of FIG. 10. Processing begins
at block 1005 where the PMSM is modeled using the field reconstruction
method with the phase currents i1 . . . i5 and the rotor
position θ to determine the normal and tangential components of the
magnetic flux density, Bn and Bt, respectively. Next, the flux
linkages, λ1 . . . λ5, are calculated at block
1010. At block 1015, a comparison is done between the flux linkage
calculation from the model, λ1 . . . λ5, and the
measurement, λ*1 . . . λ*5. At block 1020, a
decision is made whether a fault exists. If no fault exists, the process
repeats itself by looping back to block 1005. If there is a fault, the
process proceeds to fault treatment and optimal stator excitation in
block 1025.

[0056] Open-circuit fault detection: The electrical power is supplied to
the machine through the stator phases. Loss of any of the phases would
result in lower input power to the machine and hence the output torque
would be lower than expected. This lower output can potentially endanger
the machine operation. Therefore, the fault should be detected
immediately and remedial actions should be carried out. The open-circuit
fault is not a catastrophic fault as compared to the short-circuit faults
which should be cleared immediately, but the stator excitation should be
modified to compensate for the lack of energy if possible. In order to
detect the faults in the stator phases, the applied current is fed into
the FRM module. As mentioned before the normal and tangential components
of the magnetic field may be calculated using the FRM. Then these field
components are used to calculate the flux in each of the stator teeth.
The calculated fluxes are then compared to the expected values. Based on
the number of lost phases and their location, different scenarios can be
considered. FIG. 11 illustrates the arrangement of the phase windings in
the machine for a single phase open-circuit fault which takes place in
phase "A". In order to detect the fault, the sum of the 5 phase flux
linkages are considered as the fault signature. For a balanced system,
this sum is equal to zero. The fault signature for the case of the
healthy operation and phase "A" open-circuit fault is shown in FIG. 12.
In case of a single phase open-circuit fault on any stator phase, the
difference between the healthy case and the faulty case signature is a
sinusoidal whose amplitude and phase shift is different depending on the
place of fault. Table I summarizes the fault detection signatures in case
of a single phase open-circuit fault.

The same method can be used to detect double and even triple open-circuit
faults in the machine. In the case of double and triple open phases,
besides the number of open phases and their location, there is another
factor that affects the fault signature and the post fault treatment
scheme. It is important whether adjacent or nonadjacent phases are
missing. FIG. 13 depicts the winding arrangement and the fault signature
for the case of an adjacent open-circuit fault on phases "A" and "C". It
should be noted that open-circuit conditions may arise from malfunction
of the switches, clearing an inter-turn short-circuit, or an actual
open-circuit in the coils. The use of flux linkage signatures provides a
systematic approach to all potential faults in the machine.

[0057] In case of a non-adjacent double open-circuit, the signature will
be different from that of the adjacent case. FIG. 14 depicts the winding
arrangement and fault signature for this case. Table II summarizes the
fault detection signatures in case of an adjacent or non-adjacent double
phase open-circuit fault.

According to Table II, the double open-circuit faults can be determined
uniquely for each case using the proposed signature. Comparing Table II
with that of the single phase fault case, Table I, it can be seen that
the single and double phase open-circuits can be uniquely determined
using the specified signature.

[0058] Triple phase open-circuits may also occur on adjacent and non
adjacent phases. FIG. 15 depicts the winding arrangement and the fault
signature for the case of a non-adjacent open-circuit fault on phases
"A", "B", and "C". FIG. 16 depicts the winding arrangement and the fault
signature for the case of a triple open-circuit fault on phases "A", "B",
and "D". By analyzing the fault signature figures, the signature to
detect each fault can be determined as summarized in Table III.

In all cases, the flux linkages of the stator phases are calculated using
the FRM technique. Consequently, the proposed signature is calculated and
compared with each of the potential permutations that are presented in
Tables I, II, and III. Based on matching the case, the type and place of
the fault can be determined.

[0059] Partial demagnetization detection: The most important task in fault
detection is to find unique signatures that can be detected with minimal
chance of a false alarm. For this purpose, generally the quantities such
as current, voltage, etc. are monitored. In case of a healthy machine,
while the phase voltages are balanced, the sum of the voltages and
therefore the flux linkages are zero. Table IV illustrates possible
demagnetization faults in the PMSM of FIG. 1. The frequency spectrums of
the fault signature S for single magnet and double magnet demagnetization
are shown in FIG. 17 and FIG. 18, respectively. The same analysis is
performed on the other possible cases of demagnetization. It is shown
that in case of partial demagnetization, a set of frequencies would be
present in the FFT spectrum of S as summarized in Table IV.

These frequencies could be used for detection purposes. According to the
figures and data from Table IV and considering that the frequency of the
stator sinusoidal current is known, the following relationship between
the detected frequency components and type of fault can be established as
expressed in equation (18),

f dem = k 2 P f e k = 1 , 2 , 3 , ( 18
) ##EQU00010##

where P and fe are the number of magnetic pole pairs and stator
current frequency, respectively. The magnetic flux density components are
calculated using the field reconstruction method for one electrical
cycle. Then these components would be used to determine the flux passing
each stator tooth which is used to calculate the flux linkages of the
stator phases. Next, the expected flux linkages are compared with the
actual quantities. By applying the FFT and low pass filtering, the
signature frequencies can be extracted.

[0060] Rotor eccentricity detection: In the case of static eccentricity,
as the rotor is closer to a set of stator windings, the magnetic and
therefore electric balance will be lost. As a result, for the same amount
of applied current, as in the case of a healthy PMSM, some of the stator
teeth will have higher levels of magnetic flux due to the proximity to
the permanent magnets. FIG. 19 illustrates the comparison of the magnetic
flux distribution within a healthy machine 1905 and the magnetic flux
distribution within a PMSM with an eccentric rotor 1910. It can be seen
that the peak of magnetic flux is higher in case of an eccentric rotor.
Also, in case of an eccentric rotor, the distribution of the magnetic
flux around the airgap is no longer uniform. This signature can be used
to determine the eccentricity of the rotor. The unbalance in the magnetic
flux linking each stator phase can be used to detect the eccentricity.
The flux passing each stator tooth is measured using the FRM module and
compared with the flux for the healthy case and the unbalance of the flux
shows the eccentricity of the rotor. It should be mentioned that in the
case of an eccentric rotor, there is no deformity in the magnetic flux
waveform as observed in the case of PM demagnetization and the difference
is observable in the magnitude of the flux. FIG. 20 depicts the flux
passing the third stator tooth 2005 and the twentieth stator tooth 2010
in the case of an eccentric rotor compared with that of the healthy
machine. An eccentricity of 30% has been considered for the rotor in FIG.
20.

[0061] Fault treatment is an important step in optimizing machine
performance after a fault is detected and cleared. Most of the research
conducted on the fault analysis is concentrated on the methods of the
fault detection. Most researchers do not address what happens to the
system operation after the fault is detected and cleared. Some of the
fault tolerant methods suggest increasing the redundancy of the system to
compensate for the component loss in case of the fault. The redundancy of
the system is not always possible because of the limitation in the
available space or due to the high price of the equivalent replacement
device. On the other hand increasing the redundancy leads to a more
complicated control strategy which includes a higher cost of control
modules.

[0062] The optimal currents for all the fault scenarios are obtained and
stored. In case the fault occurs, based on the type and location of the
fault, the appropriate set of currents would be applied to the PMSM
stator phases to maximize the possible torque per ampere. The
optimization criteria can be changed based on the application. Here the
maximum average torque is considered while the torque ripple is
minimized.

[0063] Open-circuit fault treatment: In most applications it is desirable
to have the maximum output torque possible while the torque ripple is
minimized. This is not necessarily the most efficient way of running the
motor drive, especially in terms of losses and harmonics. Normally, the
sinusoidal excitation is used to drive the system because it introduces
fewer harmonics into the machine, hence reducing the losses and
eliminating the need of filters. However, in applications where the
maximum torque per RMS input current is targeted, sinusoidal excitation
might not be the best choice. FIGS. 21(a) and (b) represent the stator
current and torque for the case of sinusoidal excitation, respectively,
of the 5-phase PMSM machine from FIG. 1. The average torque, Tavg, for
this case is 38.88. FIGS. 22(a) and (b) represent the stator current and
torque for the case of optimal excitation, respectively, of the 5-phase
PMSM machine from FIG. 1. The average torque, Tavg, for this case is
47.6976. The FRM and the FEA models of the machine from FIG. 1 are used
in the optimization process. To obtain the optimized waveforms, the FRM
technique is used in conjunction with the MATLAB® optimization
toolbox. It can be seen that in the case of the optimal excitation, the
average output torque as well as the torque per ampere ratio are higher
than in the case of the sinusoidal excitation. Table V depicts the
numerical comparison of these two cases.

The optimization data is stored in the memory and in case of a fault the
optimal currents would be applied to the remaining healthy phases to get
the maximum torque per ampere. FIG. 23(a) illustrates the optimal current
waveforms in the case of an open-circuit fault in phase "D". FIG. 23(b)
depicts the resulting toque in the case where the optimized currents are
applied to the remaining healthy phases. The average torque, Tavg, for
this case is 39.2813. It can be seen that the average torque is decreased
compared to the case of a healthy 5-phase PMSM, but the machine still can
continue its operation with the reduced power level. The same analysis
can be accomplished for the cases where any other phases are out.
However, it is notable that 4-phase operation with optimal excitation
generates more torque than 5-phase operation with sinusoidal excitation.

[0064] Partial demagnetization treatment: Depending on the service
continuity strategy, various scenarios may be deployed after the
demagnetization fault is detected. In case the service may be provided by
another module, the machine could be stopped and the magnets be replaced.
In the case of an emergency application in which service discontinuity is
not possible, the stator currents can be modified in a way that the
maximum possible average torque could be squeezed out of the machine
shaft. Of course the presence of the harmonics in the current would
result in extra torque pulsations. For this purpose the field
reconstruction method is used in conjunction with the optimization
methods to attain the optimal current waveforms. FIGS. 24(a) and (b) show
the output mechanical torque of the machine for healthy sinusoidal
excitation and partially demagnetized, respectively. For the case of
partial demagnetization, the average torque is equal to 29.44 and has
decreased almost 25% from the average torque of 38.88 resulting from the
machine with healthy sinusoidal excitation. Additionally, torque ripple
has increased almost 30%. The MATLAB® optimization toolbox is linked
to the FRM code to determine the optimal waveforms. For each rotor
position, the optimization code calculates a set of currents based on the
optimization criteria. These currents are used to calculate the magnetic
field components in the machine. Then, using the magnetic field
components, the torque is calculated. In case the calculated torque
complies with the target values, the currents are stored and a new rotor
position is considered. FIGS. 25(a) and (b) depict the optimal stator
phase currents and the output torque, respectively, of the machine with
partially demagnetized magnets. The average torque, Tavg, for this case
is 37.8487. The optimization criteria may be chosen to achieve maximum
average torque, maximum average torque with minimum torque ripple, and
minimum torque ripple. In this embodiment, the optimization process is
targeted towards the maximum average torque. As can be seen from FIG.
24(a) and FIG. 25(b), the average torque is about 3% less than that of
the healthy machine with sinusoidal stator currents and the torque ripple
is increased as expected. Different optimization scenarios can be
considered and the optimal currents for each case can be achieved and
stored in look up tables in the control unit. Based on the application,
the appropriate currents may be applied to the stator phases in case the
fault is detected.

[0065] Rotor eccentricity treatment: In this embodiment, the goal is to
squeeze the maximum average torque out of the healthy components of the
machine. For this purpose the optimal currents for each case of the fault
are calculated. To obtain the optimized waveforms the FRM technique is
used in conjunction with the MATLAB® optimization toolbox. The
optimization criteria which are determined based on the application are
specified in the optimization code. The optimization code is linked to
FRM. Based on the initial values of the currents estimated in the
optimization module, the resulting torque is calculated and compared with
the target value. In the case the target is reached, the optimal currents
are calculated for a new rotor position. This procedure is implemented
for all the faulty cases. FIGS. 26(a) and (b) depict the sinusoidal
excitation stator currents and the corresponding output torque,
respectively, for the case with the eccentric rotor. The average torque,
Tavg, for this case is 37. It may be seen in FIG. 26(b) that the average
torque is reduced compared with the healthy machine while the ripple is
increased nearly 30%. FIGS. 27(a) and (b) depict the optimal stator phase
currents and the output torque, respectively, of the machine with an
eccentric rotor. The average torque, Tavg, for this case is 46.507. The
optimization criteria are chosen so that the torque ripple is minimized
and the average torque is maximized.

[0066] FIG. 28 is a block diagram illustrating an apparatus for detecting
faults and optimizing phase currents in an electromechanical energy
converter, in accordance with some embodiments.

[0067] In some embodiments, an apparatus 2810 comprises a processor 2815
and memory unit 2820. Processor 2815 is configured to perform
computations and general control operations and memory unit 2820 is
configured to store the optimization lookup table 2850. The computations
and the general control operations of the processor 2815 are to measure a
current of a phase of the electromechanical energy converter 2825, model
the electromechanical energy converter with the measured current input
into a field reconstruction module 2830, calculate a flux linkage of the
electromechanical energy converter 2835, compare the flux linkage with a
flux linkage from a no fault electromechanical energy converter to
determine if a fault exists 2840, and optimize the current of the phase
of the electromechanical energy converter in response to the comparison
2845.

[0068] In light of the principles and example embodiments described and
illustrated herein, it will be recognized that the example embodiments
can be modified in arrangement and detail without departing from such
principles. Also, the foregoing discussion has focused on particular
embodiments, but other configurations are contemplated. In particular,
even though expressions such as "in one embodiment," "in another
embodiment," or the like are used herein, these phrases are meant to
generally reference embodiment possibilities, and are not intended to
limit the invention to particular embodiment configurations. As used
herein, these terms may reference the same or different embodiments that
are combinable into other embodiments.

[0069] Similarly, although example processes have been described with
regard to particular operations performed in a particular sequence,
numerous modifications could be applied to those processes to derive
numerous alternative embodiments of the present invention. For example,
alternative embodiments may include processes that use fewer than all of
the disclosed operations, processes that use additional operations, and
processes in which the individual operations disclosed herein are
combined, subdivided, rearranged, or otherwise altered.

[0070] This disclosure also described various benefits and advantages that
may be provided by various embodiments. One, some, all, or different
benefits or advantages may be provided by different embodiments.

[0071] In view of the wide variety of useful permutations that may be
readily derived from the example embodiments described herein, this
detailed description is intended to be illustrative only, and should not
be taken as limiting the scope of the invention. What is claimed as the
invention, therefore, are all implementations that come within the scope
of the following claims, and all equivalents to such implementations.

Patent applications by Babak Fahimi, Arlington, TX US

Patent applications by Board of Regents, The University of Texas System